Tag Archives: analyst

3 Skills Needed for Customer Insight

While working in Amsterdam, I was reminded how insight analysts and leaders can shine brightly in very different contexts.

In the Netherlands, a mixture of training and facilitation was helping an events business. What struck me was the similarity of the challenges faced by their insight teams to the challenges I see in the U.K.

The more I work with insight leaders across sectors and geographies, the more I see how much they benefit from highly transferable skills. Here are three that are relevant to very different businesses and locations:

Prioritization

I’ve yet to work with a company where this isn’t a challenge, at least to some extent. As more and more business decisions require considering the customer, it’s not surprising that demand for data, analysis and research continues to rise. Most insight teams are struggling to meet the demand of both regular reporting (“business-as-usual”) tasks and the range of questions or projects coming in from business leaders. There have been many attempts to solve this struggle, including “projectizing” all requests (which tends to come across as a bureaucratic solution to reduce demand for information) and periodic planning sessions (using Impact/Ease Matrix or similar tools). In today’s fast-changing businesses, I’ve found that local prioritization within “the bucket method” works best.

What I mean by the “bucket method” is the identification of the silos (mainly for decision-making) that are most powerful in your business. This often follows your organizational design, but not always. Is your business primarily structured by channel, product, segment or some other division of profit and loss accounts? Each silo should be allocated a “bucket” with a notionally allocated amount of insight resource, which is based on an appropriate combination of profit potential, strategic fit and proven demand (plus acted-on results) Regular meetings should be held between the insight leader and the most senior person possible within that silo. Where possible, the insight leader should meet with the relevant director.

The bucket principle relates to the idea that, when something is full, it’s full. So, in reviewing progress and any future requirements with the relevant director, you challenge him to make local prioritization calls. Going back to the bucket metaphor, adding more requires removing something else—unless the bucket wasn’t already full. Due to human nature, I haven’t seen the bucket principle work company-wide or group-wide. However, it can work very well in the local fiefdoms that exist in most businesses. In fact, it can support a feeling that the insight team is close to the business unit and is in the trenches with them to help achieve their commercial challenges.

Buy-In

When trying to diagnose why past insight work has stalled or why progress isn’t being made, stakeholders often identify an early stage in the “project.” The nine-step model used by Laughlin Consultancy has a step (prior to starting the technical work) called “buy-in.” It takes a clear plan or design for the work needed and sends it back to the sponsoring stakeholder to ensure it will meet the requirements. Often, this practice is missed by insight teams. Even mature customer insight teams may have mastered asking questions and getting to the root of the real business need behind a brief, but they then just capture that requirement in the brief. Too few interpret that need and provide a clear description of what will be delivered.

There are two aspects of returning to your sponsor to achieve buy-in that can be powerful. First is the emotional experience of the business leader (or multiple stakeholders, if needed) feeling more involved in the work to be done. As Alexander Hamilton famously said, “Men often oppose a thing merely because they have had no agency in planning it, or because it may have been planned by those whom they dislike.” It’s so important in the apparently rational world of generating insight to remember the importance of emotions and relationships within your business. Paying stakeholders the compliment of sharing the planned work with them ensures the intended deliverable will meet their needs and is something that often helps.

The other benefit of becoming skilled at this buy-in stage is learning to manage expectations and identify communication requirements. With regard to expectations, you should set realistic timescales (which, first, requires effective planning and design), along with openly sharing any risks or issues so that they don’t come as a surprise. Communication—and asking how much a sponsor wants to be kept in the loop—can make a real difference to keeping your sponsor happy. Some sponsors will be happy with radio silence until a task is complete or a decision is needed (they value not being disturbed). Others will lose confidence in your work unless they hear regular progress updates. It’s best not to confuse one with the other.

Communication

Training customer insight analysts in softer skills often results in a significant portion of the course focusing on the presentation of findings. This isn’t surprising, because, in many ways, that’s the only tangible product insight teams can point to, prior to driving decisions, actions and business results. Too frequently, I hear stories of frustrated insight teams that believe the business doesn’t listen to them, or I hear from business leaders that their insight team doesn’t produce any real insights.

Coaching, or just listening to others express such frustrations, regularly reveals that too many analytics and research presentations take the form of long, boring PowerPoints, which are more focused on showing the amount of work that’s been done than presenting clear insights. While it’s understandable that an analyst who has worked for weeks preparing data, analyzing and generating insights wants her effort rewarded, a better form of recognition is having the sponsor act on your recommendations. Often, that’s more likely to occur based on a short summary that spares readers much of the detail.

Data visualizationstorytelling and summarizing are all skills necessary to master on the road to effective communication. Most communication training will also stress the importance of being clear, concrete, considerate, courteous, etc. Many tabloids have mastered these skills. Love them or hate them, tabloid headline writers are masters of hierarchies of communication. Well-crafted, short, eye-catching headings are followed by single-sentence summaries, single-paragraph summaries and then short words, paragraphs and other line breaks to present the text in bite-sized chunks.

Transferable skills

Insight analysts and leaders who master such crafts as prioritization, buy-in and communication could probably succeed in almost any industry and in many different countries. Many directors will attest to the fact that sideways moves helped their careers. A CV demonstrating the ability to master roles in very different contexts is often an indication of readiness for a senior general management role.

The One Thing to Do to Innovate on Claims

If you love football, then you know how frustrating it is to be a football fan. Every offseason, you get excited about the potential for the coming season. Before the season begins, you read all of the articles and watch the analysts.

They all say, “This is the year.” Your team added some of the top defensive players in the league. You’re convinced the team has solved its offensive woes, too. Your team added a star wide receiver, and the running back is looking great in training camp.

Then the season starts, and your team suffers loss after loss. You question how professionals can spend so much time and money on the sport yet fail to improve. As the season continues to sputter, more and more people call for the team to fire the coach. At the end of the season, they fire the coach and hire a new star coach from a great team.

“Next year,” you and the rest of the fan base tell each other.

The next season begins and your team still loses. Year after year, the cycle repeats itself.

When it comes to innovation, insurance company claims departments have a lot in common with your favorite underachieving football team. Top talent in every department. Great recruits from top companies. Lots of talk about the newest technology. But each year you get the same results.

How can you solve this problem?

The One Thing

In “The One Thing,” Gary Keller shares several lessons we should apply to the insurance claims industry. He does so by simplifying the decision-making process. Whether you’re the general manager of a football team or an insurance claims executive, you can apply Keller’s lessons to your situation.

The Six Lies Between You and Success:

  1. The idea that everything matters equally;
  2. Multitasking;
  3. Lack of discipline;
  4. The belief that willpower is always on will-call;
  5. A balanced life;
  6. The idea that big is bad.

These “Six Lies” insurance claims departments. Claims professionals will get what they put in each day. If that’s emailing about hundreds of claims, then claims professionals will get routine claim maintenance. They will not achieve innovation. By making routine claim maintenance the priority, claims departments are falling victim to the six lies standing between the claims department and innovation.

The Four Thieves of Productivity:

  1. Inability to say “No”;
  2. Fear of chaos;
  3. Poor health habits;
  4. An environment that doesn’t support your goals.

While I can’t make any assumptions about whether there are poor health habits in your claims departments (unless your claims professionals are gorging on the vendor-sponsored food!), I can assume that the four thieves should resonate with you.

Insurance claims professionals do what they do because that’s what everybody has always done. No one has ever been terminated for saying “yes” to a responsibility. People who follow the status quo feel safer than people who hinge their success on a business transformation. As a result, claims departments are productive at claims maintenance, but they often leave much to be desired when it comes to innovation.

The Focusing Question

Keller condenses the entire book into what he calls “The Focusing Question.”

What’s the one thing you can do now such that by doing it everything else will become easier or unnecessary?

Good questions are the path to great answers. By combining a small focus with a big goal, the “Focusing Question” provides you with the ideal starting point to achieve something great.

Claims innovation requires starting with “The One Thing” today: giving your best claims manager responsibility for transforming the claims department. While this may sound drastic, it truly is “The One Thing” that will transform an insurance company. I’ve seen it. With a strong leader dedicated to this project, executives will breeze through the process of selecting vendors, identifying key requirements, troubleshooting workflows and handling anything that stands in the way of true innovation.

Once “The One Thing” is addressed, many tasks will follow: assigning a good leader from the IT department, engaging an outside consultant and supporting the department with future-focused software. But until executives dedicate their best claims manager to “The One Thing,” claims departments will suffer from unnecessary obstacles.

Claims departments and football teams will keep underachieving until they get their franchise quarterbacks. You can hire all the star free agents and coach your teams to change, but if your quarterback spends his time focusing on the same old plays, get ready for another year with the same results.

Who will be your company’s Tom Brady?

Frustrated on Your Data Journey?

It’s going to take how much longer?! It’s going to cost how much more?!!

If those sound like all too familiar expressions of frustration, in relation to your data journey (projects), you’re in good company.

It seems most corporations these days struggle to make the progress they plan, with regards to building a single customer view (SCV), or providing the data needed by their analysts.

An article on MyCustomer.com, by Adrian Kingwell, cited a recent Experian survey that found 72% of businesses understood the advantages of an SCV, but only 16% had one in place. Following that, on CustomerThink.com, Adrian Swinscoe makes an interesting case for it being more time/cost-effective to build one directly from asking the customer.

That approach could work for some businesses (especially small and medium-sized busineses) and can be combined with visible data transparency, but it is much harder for large, established businesses to justify troubling the customer for data they should already have. So the challenge remains.

A recent survey on Customer Insight Leader suggests another reason for problems in “data project land.” In summary, you shared that:

  • 100% of you disagree or strongly disagree with the statement that you have a conceptual data model in place;
  • 50% of you disagreed (rest were undecided) with the statement that you have a logical data model in place;
  • Only 50% agreed (rest disagreed) with the statement that you have a physical data model in place.

These results did not surprise me, as they echo my experience of working in large corporations. Most appear to lack especially the conceptual, data models. Given the need to be flexible in implementation and respond to the data quality or data mapping issues that always arise on such projects, this is concerning. With so much focus on technology these days, I fear the importance of a model/plan/map has been lost. Without a technology independent view of the data entities, relationships and data items that a team needs to do their job, businesses will continue to be at the mercy of changing technology solutions.

Your later answers also point to a related problem that can plague customer insight analysts seeking to understand customer behavior:

  • All of you strongly disagreed with the statement that all three types of data models are updated when your business changes;
  • 100% of you also disagreed with the statement that you have effective meta data (e.g. up-to-date data dictionary) in place.

Without the work to keep models reflecting reality and meta data sources guiding users/analysts on the meaning of fields and which can be trusted, both can wither on the vine. Isn’t it short-sighted investment to spend perhaps millions of pounds on a technology solution but then balk at the cost of data specialists to manage these precious knowledge management elements?

Perhaps those of us speaking about insight, data science, big data, etc. also carry a responsibility. If it has always been true that data tends to be viewed as a boring topic compared with analytics, it is doubly true that we tend to avoid the topics of data management and data modeling. But voices need to cry out in the wilderness for these disciplines. Despite the ways Hadoop, NoSQL or other solutions can help overcome potential technology barriers — no one gets data solutions for their business “out of the box.” It takes hard work and diligent management to ensure data is used & understood effectively.

I hope, in a very small way, these survey results act as a bit of a wake up call. Over coming weeks I will be attending or speaking at various events. So, I’ll also reflect how I can speak out more effectively for this neglected but vital skill.

On that challenge of why businesses fail to build the SCVs they need, another cause has become apparent to me over the years. Too often, requirements are too ambitious in the first place. Over time working on both sides of the “IT fence,” it is common to hear expressed by analytical teams that they want all the data available (at least from feeds they can get). Without more effective prioritization of which data feeds, or specifically which variables within those feeds, are worth the effort – projects get bogged down in excessive data mapping work.

Have you seen the value of a “data labs” approach? Finding a way to enable your analysts to manually get hold of an example data extract, so they can try analyzing data and building models, can help massively. At least 80% of the time, they will find that only a few of the variable are actually useful in practice. This enables more pragmatic requirements and a leaner IT build which is much more likely to deliver (sometimes even within time & budget).

Here’s that article from Adrian Swinscoe, with links to Adrian Kingwell, too.

What’s your experience? If you recognize the results of this survey, how do you cope with the lack of data models or up-to-date meta data? Are you suffering data project lethargy as a result?

Are Softer Skills for Analysts Neglected?

Are you neglecting the development of softer skills in your analysts? Based on conversations with customer insight leaders, including at the very pleasant DataIQ Talent Awards, it would seem you are. When I shared the experience of Laughlin Consultancy, that training for analysts in softer skills is our most popular service, these leaders were not surprised. But if there is such widespread support for the idea, why haven’t businesses invested in this training sooner?

People have suggested a number of theories:

  • Underinvestment in these teams or in training during lean times
  • Softer skills not valued by some geekier analysts or leaders
  • Skepticism from line managers (especially CMOs) as to what value such training would deliver
  • Just too busy!

All these are understandable challenges or excuses, and more than one resonates with me from my time creating and leading large customer insight teams. Perhaps there is another reason, as well. In my new line of work, I get to speak at industry conferences, read data/analytics/research publications and scan the plethora of blogs or social media comments on this topic. What becomes clear when consuming these is that the “buzz” or fashion is to focus on the technical. Ever since Google made “data scientist” the sexy job title for the decade, both suppliers and users have obsessed over technology and technical skills.

Following the comforting old maxim, “it’s what you do with it that counts,” I worry about this fetish with all things techie. As an Apple addict, I can empathize with the attraction of new shiny technology and beautiful design. However, I’m sure we’d all agree that commercial leaders should be focused on outcomes, not tools.

This recent fascination with “big data” or “predictive analytics” or “data scientists” is also worryingly reminiscent of what happened during the customer relationship management (CRM) bubble. When that term was in vogue, businesses were falling over each other to “do CRM,” which a number of large technology suppliers made sure equated with buying a CRM system. Not surprisingly, with hindsight, most of these CRM projects failed, and systems did not repay that hefty price tag.

Given that most of us are keen to avoid repeating mistakes, it’s a pleasure to report that more and more switched-on businesses are realizing that they can’t just hire technically competent graduates and get the insight their business needs.

So, what do I mean by softer skills? Maybe not precisely what you might come up with, but I hope the list below is familiar. Laughlin Consultancy’s most popular service in the first half of 2015 was the delivery of a “consultancy skills for analysts” training course that includes theses elements:

Have you invested in training like that for your analysts? What results have you seen?

Another way to think about this issue is, what distinguishes your top talent from those analysts who prove to be just so-so? My experience is that it’s capability in these softer skills. Over the years, I’ve met or employed hundreds of analysts, and while many may be a whiz at coding or have mastered model building in SAS, few are great communicators who really get what the business needs. Those who did master the skills I’ve outlined above went on to not just be effective consultants within their business; many are now leaders themselves.

Is that your experience, or would you identify other training needs for your team?